Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle. Quantifying changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies. However, fine resolution spatial data of CEs deficient. This study quantified CEs from 15 types of energy sources, including residential, tertiary, and industrial sectors in Shanghai. Additionally, we mapped the CEs for the three sectors using point of interest data and web crawler technology, which is different from traditional methods. At a resolution of 30 m, the improved CEs data has a higher spatial resolution than existing studies. In addition, the method of this study is better than traditional methods with details and heterogeneity of CEs, the CEs distribution close to reality. The results indicated that there was a consistent increase in CEs during 2000–2015, with a low rate of increase during 2009–2015. The intensity of CEs increased significantly in the outskirts of the city, mainly due to industrial transfer. Moreover, intensity of CEs reduced in city center. Decoupling between the economic development and CEs in the city was observed during in 2000–2015 owing to technological progress and reduction in CEs intensity.
Research Article
A Fine Spatial Resolution Modeling of Urban Carbon Emissions: A Case Study of Shanghai, China
https://doi.org/10.21203/rs.3.rs-1001811/v1
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carbon dioxide emissions
energy consumption
spatial distribution
POI
Greenhouse gas emissions, such as carbon dioxide, contribute to global warming (Malik et al., 2016), which is a threat for humans (IPCC, 2013; Ma et al., 2019). Studies have shown that urban areas account for 2% of the total land area, but produce 80% of the greenhouse gas emissions (Lombardi et al., 2017; Balk et al., 2005). In 2014, the urban population exceeded 65% of the global population (The United Nations, 2014). Cities are the main areas of human activity and greenhouse gas emissions (Li et al., 2018). To cope with global climate change and protect the environment, greenhouse gas emissions have drawn increasing attention (Wang et al., 2017a). However, urban expansion and population growth have driven increasingly intensified carbon dioxide emissions (CEs) in cities, particularly in China (Zha et al., 2010). In recent decades, China has experienced rapid economic growth, environmental damage, and massive energy consumption. In 2014, the CEs of China were 10.29 billion tons, 4.21 times that in 1990, far higher than those of the United States (5.25 billion tons) and other developed countries (https://data.worldbank.org).
To date, extensive validation of CEs and their environmental effects have been conducted (Baumert et al., 2019; Hamilton and Friess, 2018), and measuring CEs from energy consumption has received increasing attention (Chen et al., 2017; Gudipudi et al., 2016; Zhang et al., 2019). For example, Lu (2018) quantified and analyzed the impact of CEs from fossil fuel consumption at the national level. Wang (Wang et al., 2015) analyzed the driving factors of CEs in China. Sufficient research has been conducted on carbon dioxide accounting. Methods of measuring CEs from various processes are developed. Three methods are widely used for carbon accounting: bottom-up, top-down, and hybrid approaches (Gusti and Jonas, 2010; Deng et al., 2011; Andersen et al., 2019). The top-down method is mainly a production-based accounting approach and is extremely useful for quantifying energy flows between regions and countries (Lombardi et al., 2017). Although the top-down method requires less time and labor, it is not suitable for carbon accounting of small systems. The bottom-up method is used for a consumption-based accounting method, which is based on resource consumption at each step of human activity and life cycle analysis (LCA) (Lombardi et al., 2017). The bottom-up method has a relatively high accuracy, but time-consuming and requires a large amount of data (Lombardi et al., 2017). The hybrid method includes the synthesis of bottom-up, top-down, and other methods, such as environmental input-output analysis (EIOA)/LCA (Kjaer et al., 2015). These integrated approaches are suitable for carbon dioxide accounting when detailed energy data are not available.
Although there is extensive research on CEs, the methods are mainly focused on accounting and analyze the relationship between CEs and socio-economic. However, Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle (Gurney et al., 2009; Crippa et al., 2020). Moreover, quantifying the changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies (Chuai and Feng, 2019). More recently, mapping CEs has gained attention among researchers. With the development of remote sensing technology, carbon monitoring satellites have realized the dynamic monitoring of large-scale greenhouse gas emissions (e.g., greenhouse gas observation satellites, GOSAT). GOSAT's sub-satellite spatial resolution is approximately 10.5 km, and it is suitable for monitoring carbon dioxide concentrations on region and global. However, GOSAT images are not suitable for studying urban-scale CEs. Additionally, nighttime lighting (NTL) data sets are widely used to map CEs (Wang and Li, 2017; Su et al., 2014; Racitia et al., 2014; Zhang, et al., 2013), as there is a close relationship between NTL and population density, where a higher NTL value indicates greater energy consumption (Sutton et al., 2003; Zhuo et al., 2009). The spatial CEs distribution of existing studies has relied on spatial proxies to downscale the total CEs to a grid. Linear regression and panel regression models are the two most commonly used methods (Shi et al., 2016; Cui et al., 2019; Han et al., 2018). The NTL downscale method mapping CEs has a resolution of 1 km, the CEs distribution lack of more spatial details and unable to distinguish industrial, tertiary, and residential CEs. Moreover, Cai (2018) developed the China high-resolution emission database (CHRED), which was constructed using the bottom-up method and a spatial resolution of 1 km, combined socio-economic data. Most of the existing spatial CEs studies were mapped based on the NTL at a spatial resolution of 1 km. Thus, NTL-based CEs distribution still has limitations. On one hand, the distribution of CEs does not reflect further details of its spatial heterogeneity at the city level. Low spatial resolution cannot satisfy the application of fine urban carbon management and emission reduction strategy development. On the other hand, the downscaling method of CEs does not distinguish sectors CEs. To improve the spatial resolution of CEs, researchers have set up monitoring sites to accurately monitor CEs (Cai et al., 2019). In recent years, emerging big data has provided an opportunity to study the spatial characteristics of high-resolution urban CEs. Web crawler technology (WCT) can be used to obtain information about the location and attributes of the CEs. In addition, points of interest (POI), which contain multi-attribute information and location, is an important data source. For example, Gao(2021) used POI to mapping urban carbon emissions. Power point and industrial facilities were used to map CEs at high resolutions (Cai et al., 2014). In summary, existing CEs distribution research focus on region and global, based on NTL with 1 km resolution. So, urban and fine resolution CEs data is still rare.
In order to meet the critical needs of carbon cycle, urban precision carbon reduction strategies and carbon management, in this study, with the help of POI data, we developed an improved method and mapped urban CEs with a fine resolution. Compare to traditional NTL-based method, the CEs distribution of this study has better accuracy and fine resolution. Moreover, different sectors CEs distribution is clear. We selected the international city of Shanghai as an example to map the CEs and validate the method. This study used POI data and GIS technology to map the fine resolution urban CEs distribution. First, according to data availability and accuracy requirements, this study applied the bottom-up method to account urban CEs for residential, tertiary, and industrial sectors from 2000 to 2015. Moreover, this study used the POI data and multi sources data which obtained by web crawler technology (WCT). The method of mapping CEs in this study could clearly identify the CEs spatial patterns of each sector, and different from existing studies. We analyzed the spatial patterns of CE in Shanghai. We believe that this methodology could be applied in other fast-growing cities to understand carbon cycle and develop accuracy urban carbon reduction strategies (Wang et al., 2014).
2.1 Study area
Shanghai is one of the megacities in the world. At the end of 2018, there were more than 24.24 million people living in Shanghai within the area of 6340.5 km2. It lies at the front edge of the Yangtze River Delta (YRD). The gross domestic product (GDP) increased more than 31 times from 1990 to 2015. At the same time, rapid urbanization and industrialization caused a sudden surge in energy consumption and an increase in the urban population. The development of the social economy, expansion of the city, and an increase in the population have led to an increase in CEs. Balancing environmental change and socio-economic development is an urgent need for many cities, particularly in mega cities such as Shanghai. As a resource-limited city, Shanghai, its energy mainly depends on imports. Therefore, understanding the distribution of CEs and reducing energy consumption are important ways to reduce urban CEs and achieve sustainable urban development. The findings and methods from this study will help conduct analyses for other cities, particularly developing cities.
2.2 Materials
To estimate the CEs distribution in Shanghai, geospatial and statistical data were used. Geospatial data included POI and land use data. The POI data for 2000, 2005, 2010, and 2015 were in vector format. This was obtained from the Gaode map (https://ditu.amap.com/) using Application Programming Interface (API) technology. We used 2002 POI data instead of 2000, because the earliest POI data is 2002. The POI data includes the residential, industrial, and tertiary sectors points. Residence community (RC) POI and industrial enterprise (IE) POI contain residential location information and type of enterprise, respectively. The residential area was estimated using statistical data from the Shanghai Statistical Yearbook (SSY) and the website of residential rental companies (e.g., https://sh.lianjia.com/) using WCT. The land use data were in raster format at a spatial resolution of 30 m, which were obtained from the Resource and Environment Data Cloud Platform (REDCP, http://www.resdc.cn/AchievementList.aspx). The land use data included cropland, forest, grass, water, construction land, and unutilized land.
Statistical data included the energy balance tables (EBTs) of Shanghai, population (POP), GDP, resident consumption, and energy consumption of key energy-consuming industrial enterprises (ECKEIE, Table 1) of Shanghai. These data were obtained from SSY and are available online (http://www.cnki.net/). The EBT contains data on more than 15 types of energy consumption from industrial, tertiary, and residential sectors.
| Industrial type | National average energy consumption per 10,000 yuan (ton of standard coal) | Carbon emissions factor(%) |
|---|---|---|
| Oil and gas extraction industry | 4.59 | 7.78 |
| Food processing industry | 1.17 | 1.98 |
| Food manufacturing | 0.71 | 1.20 |
| Beverage manufacturing | 0.79 | 1.34 |
| Tobacco processing industry | 0.13 | 0.22 |
| Textile industry | 0.99 | 1.68 |
| Clothing and other fiber products manufacturing | 0.26 | 0.44 |
| Wood processing and bamboo, rattan, palm and grass products industries | 1.6 | 2.71 |
| Paper and paper products industry | 2.05 | 3.47 |
| Petroleum processing and coking industry | 3.92 | 6.64 |
| Chemical raw materials and chemical products manufacturing | 4.2 | 7.12 |
| Pharmaceutical manufacturing | 0.56 | 0.95 |
| Chemical fiber manufacturing | 0.88 | 1.49 |
| Rubber products industry | 0.49 | 0.83 |
| Plastic products industry | 0.7 | 1.19 |
| Non-metallic mineral products industry | 5.58 | 9.46 |
| Ferrous metal smelting and rolling processing industry | 4.64 | 7.86 |
| Non-ferrous metal smelting and rolling processing industry | 3.27 | 5.54 |
| Metal products industry | 0.52 | 0.88 |
| General machinery manufacturing | 0.48 | 0.81 |
| Special equipment manufacturing | 0.58 | 0.98 |
| Transportation equipment manufacturing | 0.18 | 0.31 |
| Electrical machinery and equipment manufacturing | 0.11 | 0.19 |
| Electronics and communication equipment manufacturing | 0.09 | 0.15 |
| Production and supply of electricity, steam and hot water | 9.28 | 15.73 |
| Gas production and supply industry | 5.93 | 10.05 |
| Tap water production and supply industry | 5.3 | 8.98 |
| Note: The data source is Shanghai Statistics Bureau; The weight calculated according to percentage of national average energy consumption. | ||
3.1 Estimating carbon dioxide emissions
The CEs of industrial, tertiary, and residential sectors at the year-end were estimated based on the EBT. We selected 15 types of primary energy sources from the energy balance sheet as the accounting source of carbon dioxide. These included raw coal, clean coal, briquette, other coal washing, coke oven gas, coke, other gases, kerosene, crude oil, diesel, gasoline, liquefied petroleum gas, fuel oil, natural gas, and refinery dry gas. The CEs for each sector were estimated according to the number of energy product consumption, carbon content per unit energy calorific value, oxidation rate during energy combustion, and carbon dioxide emission factor (Eq 1) (Meng et al., 2017).
3.2 Mapping carbon dioxide emissions
To understand the spatial change in CEs, we mapped the CEs at the urban scale. For each grid cell, the CEs was mapped according to the sectors and carbon dioxide emission intensity index (CEI) of the POI. The CEI of RC and IE were calculated based on residential area and ECKEIE, respectively, and reflect energy consumption intensity. Due to the lack of data on tertiary energy consumption intensity, we used the value “1” as the CEI of tertiary POI. Here, we assumed that all tertiary POIs had the same CEI. The CEI of all sectors was calculated using Eqs. (2) and (3) (Silverman, 1986).
According to the above-mentioned equations used to calculate the energy consumption density for the residential, tertiary, and industrial sectors, mapping the CEs (see Fig. 1) involved combining the CEs calculated from Eq. (1). As shown in Fig. 1, the urban boundary was extracted from land use data, and the boundary was used to clip the energy consumption intensity map, and the CEs boundary was the same as the urban boundary. We allocated CO2 according to the energy consumption intensity for each sector, mapping the CEs, and then calculated the total carbon dioxide by overlay analysis.
4.1 Changing patterns of CEs
The total CEs in Shanghai increased by 64% from 112.36 Mt to 183.75 Mt, 2000-2015, with a 50% and 331% increase in the population and GDP, respectively (Fig. 2a). The GDP growth rate gradually decreased from 12–7.98% during 2000–2015. Similarly, the rate of increase of total CEs decreased from 5.12–2.12%. The population growth rate increased from 3.28–3.99% from 2000 to 2009 and then dropped to 1.5% from 2009 to 2015 (Fig. 2a). Typically, the GDP growth rate increases faster than the CEs growth rate. A relative decoupling of CEs and GDP can be observed, and this decoupling gradually strengthens (Fig. 2a). In this study, the total CEs comprised of three parts: industrial, tertiary, and residential consumption (household consumption) (Fig. 2b). Industrial CEs increased from 95.9 million tons to 135.58 million tons, but its percentage decreased from 85.35–73.79% during 2000–2015. The residential CEs increased from 11.46–18.14%. The total CEs dropped significantly in 2009 owing to a decrease in industrial CEs. In 2009, industrial CEs decreased by more than 15%, likely because of the global financial crisis in 2008 (Gökay and Whitman, 2010). The global financial crisis triggered a global recession, which had an impact on the economy in Shanghai as well. The total CEs decreased by 11.56%, and the average growth rate of industrial CEs was 1.60% during 2009–2015, mainly due to the global financial crisis.
4.2 Changes in the spatial pattern of CEs
CEI increased in the city center area from 2000 to 2005, with the highest CEI increasing from 0.62 to 0.66 Mt/km2 (Fig. 3). However, a turning point appeared in 2005, when the CEI was reduced with the expansion of urban areas. In 2015, CEI continued to decrease. In spite of the decreasing CEI in the city center, we take the area within 10 kilometers of Shanghai People's Square as the center of the city. The total CEs increased by 39.75 million tons from 2005 to 2015, and city expand 476 km2. In contrast, the CEI of the non-city center area increased significantly from 2005 to 2015. Thus, we concluded that urban expansion and industrial relocation together reduced the CEI in the urban center, mainly due to the shift of industrial enterprises from urban center to urban suburbs. However, total CEs continued to increase.
To analyze the change in spatial patterns of CEs between 2000 and 2015, we applied the city center as the center of circle, then we drew a buffer zones for each 10 km (according to the spatial distribution of population density) and counted the CEs within each buffer zone (Fig. 4a). From 2000 to 2015, the total CEs increased to 50.79 Mt (1.76%) in the city center (between 0 and 10 km). However, a remarkable increase (75.81%) was observed in peri-urban areas (distance to the city center between 10 and 20 km, Fig. 4e). Moreover, CEs increased rapidly in the suburbs (distance to the city center from 20 to 70 km). This indicates that human activities spread towards peri-urban areas with urban expansion. When examining industrial CEs, we found that CEs significantly reduced (41.05%) in the city center areas. We also observed an increase (19.12 Mt, 138.09%) in the suburbs (distance to the city center from 20 to 30 km) from 2000 to 2015 (Fig. 4c). Additionally, industrial CEs in the areas between 30 and 70 km from the urban center generally increased rapidly (Fig. 4c). This could be explained by industrial transfer, where in the industries were moved to peri-urban areas of Shanghai and other provinces (Li, 2011). During the “Twelfth Five-Year Plan” period, due to the continuous increase in land and labor costs, tertiary costs, the pressure of resources, environment, and the transformation of its economy and industrial structure, industries shifted to the suburbs of Shanghai and nearby cities to achieve a cross-regional industrial division of labor. This trend is becoming increasingly apparent (http://www.shanghai.gov.cn). In addition, industrial transfer was mainly associated with high energy consuming factories, whereas labor-intensive factories remained in the city center of Shanghai, such as iron and steel enterprises. Thus, while the CEs increased in non-city center areas, they decreased in the city center areas (http://www.shanghai.gov.cn). Interestingly, the residential CEs increased in both the city center and its surrounding areas, particularly in the western part of the city center (Fig. 4d). The largest increase (8.70 Mt) in residential CEs was observed in peri-urban areas (distance to the city center from 10 to 20 km), followed by the city center areas (7.67 Mt). There was a rapid increase in the suburbs situated at a distance of 30–70 km from the center. This clearly indicates that CEs increased with urban expansion between 2000 and 2015. In addition, compared to 2000, population density and human activity intensity also increased in the city center. The CEs from the tertiary sector mainly increased in the city center (5.75 Mt) at a rate of 204.58% (Fig. 4b), primarily as the tertiary sector located in the city center and the intensity of tertiary activity has increased. In addition, the suburbs (distance to the center from 50 to 60 km) showed the fastest growth, followed by the areas located 30 to 40 km from the center.
The intensity of CEs is increasing from urban centers to suburbs, and the spatial distribution area of CEs has increased significantly. This is due to industrial transfer and economic transformation and upgrading. The analysis of the urban spatial morphology (Table 2) shows that from 2000 to 2015, the spatial distribution of the city increased in connectivity, agglomeration, they indicate that Shanghai is more compact. The spatial characteristics of CEs are consistent with the urban form. Compact cities are conducive to the development of public transportation and change people's travel behavior, thereby reducing carbon emissions (Zhang et al., 2018; Xia et al., 2020). Moreover, reasonable land use and functional allocation can effectively reduce carbon emissions.
|
SFI |
2000 |
2005 |
2010 |
2015 |
|---|---|---|---|---|
|
CONTAG |
46.87 |
46.88 |
56.74 |
57.84 |
|
SPLIT |
4.53 |
4.53 |
2.18 |
2.20 |
| Where CONTAG is Contagion index, high value is high connectivity; SPLIT is Splitting index, high value is more broken and looser. CONTAG and SPLIT were calculated by Fragstas 4.2.1. | ||||
4.3 Accuracy evaluation
In order to evaluate the accuracy of CEs estimation under this study (POI-based CEs), we used CEs obtained by the NTL-based model method (NTL-based CEs), which maps CEs based on statistical model and NTL data, and then compared to POI-based CEs (Fig. 5). There is a lack of fine-resolution spatial data on CEs, even though satellite platforms such as GOSAT directly observe CEs. The NTL-based model is a good method for mapping CEs at the regional scale, with a resolution of 1 km. The NTL-based CE was produced according to Meng (2017) and employed as a standard for assessing CEs in this study. The resolutions of POI-based CEs and NTL-based CEs are 30 m and 1 km, respectively. We employed 1 × 1 km grids to extract CEs from the two methods, and then mapped them using a scatterplot, as shown in Fig. 5.
It is clearly to know that approximately 31.56% of the pixel cells of the POI-based, the were larger than the values of NTL-based CEs pixels. The highest value in NTL-based CEs was 122.49 kt. We set the low range as 0–30% of the highest value, which from 0 to 36.75 kt. Moreover, the high range is 80–100% of the highest value, which between 85.75 and 122.49 kt. According to Fig. 5, there are more than 40.89% of the total pixels in the low range, the values of NTL-based CEs were larger than POI-based. Additionally, in the high range, more than 15.20% of the total pixels’ value of POI-based CEs larger than NTL-based. These results indicate that the NTL-based method underestimated CEs in the high range and overestimated CEs in the low range, compare to the POI-based method, which may be caused by the spillover and saturation effects of NTL data.
Figure 6 compares the distribution of CEs in 2010 based on the two methods. It can be sees that the area of NTL-based CEs is larger than the POI-based CEs (Fig. 6a-c). The reason is the overflow effect and resolution of NTL data. In addition, the boundary of POI-based CEs is in accordance with urban boundary extracted using land use data. Because of POI-based CEs was calculated along with land use data to confirm the spatial distribution of CEs. This means that CEs distribution of this study has a fine resolution and close to the real boundary of CEs. Many pixels value of POI-based CEs was larger than NTL-based CEs in city center areas, and some pixels lower than NTL-based CEs in suburbs (Fig. 6d). This indicating that the traditional NTL-based method underestimates CEs in high emissions areas and overestimates CEs in low emission areas, compare to POI-based method.
We compared the CEs distribution of NTL-based and POI-based method, the CEs distribution of POI-based was found to have more details and fine resolution. Additionally the heterogeneity of the spatial distribution in the three sectors could also be assessed at a fine scale (Fig. 7). The spatial distribution range and characteristics of industrial (Fig. 7d), tertiary (Fig. 7e), and residential (Fig. 7f) CEs based on traditional method are similar and consistent with the NTL data. In contrast, the spatial patterns of CEs by POI-based is different from each sector, and the spatial heterogeneity of CEs for each sector could be accurately assessed (Fig. 7a-c). This is impossible with traditional methods.
To verify accuracy of CEs by POI-based method, we employed a sampling line (see Fig. 8a) to extract CEs for the two methods, and compared the values (see Fig. 8b). It is worth noting that Baosteel, a steel factory in Shanghai, which is a high emission source, is located at the sampling line. The POI-based CEs (this study) correctly showed a sudden change and reflected the associated emissions, but the NTL-based method not significant (Fig. 8b). In addition, the CEs of NTL-based is a smooth data line compare to CEs of POI-based. The latter has significant difference, this basically consistent with facts. Because of the sampling line passes through the built-up areas and farmland, so the CEs are striking differences.
5.1 Uncertainties and limitations of this method
The distribution of urban CEs was assessed based on POI data and its energy consumption intensity. The uncertainty of the POI data and attribute data was consistent with the uncertainty of the distribution of CEs. In addition, energy consumption data and CEs parameters obtained from the existing literature and materials also have some limitations. As the energy consumed in city areas is imported from other cities and areas, energy use types and energy tastes are different. Therefore, there are limitations in using statistical data and uniform CEs parameters to calculate CEs. Moreover, because of the lack of high-resolution remote sensing data of CEs as reference data, it is difficult to accurately assess the accuracy of our estimations.
5.2 Potential uses
Based on the fine resolution mapping method of carbon dioxide emissions and dataset at the urban scale, which was proposed in this study, several potential uses could be obtained. They include, but are not limited, the following.
First, this method could be applied to map the distribution of urban CEs and as a decision support tool for the analysis of spatial-temporal changes in high-resolution urban CEs in other cities. Second, high-resolution urban CEs data could be used to understand the relationship between spatial-temporal changes and social economy. Third, fine-resolution urban CEs data could be used as a data source for urban spatial planning, also useful to develop accurate urban carbon management strategies. In addition, fine resolution CEs data could meet the critical need of carbon cycle, climate change research, and emerging flux inversion.
The goal of this study was to propose an improved method for mapping a fine resolution urban CEs. We found that results obtained using our method had a higher resolution than the traditional NTL-based method. In addition, the method of this study is suitable for mapping the CEs of sectors. This case study in Shanghai suggests that the CEs method based on POI is suitable for the high-resolution spatial distribution under province level. The method effectively draws the spatial characteristics and heterogeneity of urban CEs, and its spatial resolution is better than traditional method. It is important to point out that the POI-based method can accurately determine both emission extent and intensity, be able to identify the carbon emissions of different sectors. In addition, POI-based CEs draws more details and close to reality. Therefore, the improved method is better than the traditional method for mapping CEs. We also found that the total CEs in Shanghai witnessed a three-stage increase from 2000 to 2005, and a steady growth from 2005 to 2009. The global financial crisis strongly impacted CEs growth from 2008 to 2009. The global financial crisis markedly decreased CEs from 2008 to 2009, particularly from the industrial sector. A significant relationship between economic growth and CEs was also observed. The industrial sector was found to be the largest CEs contributor, followed by residential and tertiary sectors. Industrial CEs have shaped the trend in total CEs. These industrial CEs slightly decreased in the city center, but increased in the peri-urban areas. The CEs of residence and tertiary areas significantly accumulated in city center regions and increased rapidly in suburbs.
CRediT authorship contribution statement
Cheng Huang: Formal analysis, Writing-original draft, Methodology, Data curation. Qianlai Zhuang: Data curation, Writing-review & editing. Xing Meng: Writing-review & editing, Reviewing. Peng Zhu: Writing-review. Ji Han: Editing, Conceptualization, Revising, Writing-review & editing. Lingfang Huang: Writing-review; All authors contributed to results interpretation and paper writing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The work was supported by the National Key R&D Program of China ( 2017YFC0505703); Shanghai Committee of Science and Technology Fund (19DZ1203303); Science and Technology Project of Education Department of Jiangxi Province (GJJ200412) and the National Natural Science Foundation of China (41801314). Cheng Huang acknowledge the financial support from China Scholarship Council (201706140147) and logistics support from Purdue University.
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No competing interests reported.